Rippit vs. Gong for Conversation Analysis and Agents (2026): Differences, Pricing, and When to Use Both
Short answer: Gong is the system of truth for revenue teams, running revenue workflows such as forecasting, deal management and rep coaching.[1]
Rippit is a Growth Data Platform.[2] It reads every conversation in the sources you connect, from Gong calls to support tickets, chats and CS calls, turns what it finds into structured data[3] and keeps it current next to your pipeline, product usage and competitor data. Any team can define a new field in plain English and drill into it until they find the root cause.[4]
Many teams use both: they keep Gong for revenue workflows and add Rippit to ask questions across the whole customer journey.[5] Rippit starts free with $10 in credits.[6] Gong pricing is by quote.[7]
They overlap on analysis of sales calls. Rippit differs in what it puts next to those calls: tickets, renewals and usage.
Rippit vs. Gong at a glance
| CRITERIA | Gong | Rippit |
|---|---|---|
| What is it? | Revenue AI platform: conversation intelligence, forecasting, sales engagement, enablement and AI agents[1] | Growth Data Platform: your conversations and business data in one place, with agents your teams set up[2] |
| Primary team | Sales and revenue teams, including post-sales customer success[8] | Sales, marketing, product, CS and support teams, working from the same data |
| Starting point | Sales calls, meetings and emails, tied to deals and accounts[9] | The conversations you already have: tickets, calls and chats, including the calls you record in Gong[10], plus CRM and pipeline, product usage and your own documents, such as battle cards |
| Conversations covered | Calls, meetings and emails.[9] Its Zendesk integration imports Zendesk calls and creates support tickets from Gong call transcripts[11] | Sales calls from Gong alongside support tickets, chats and CS calls, in one analysis[10] |
| Best fit | Revenue leaders who run forecasting, deal reviews, sales engagement and rep coaching in one platform | Sales and revenue leaders who need to see how what's said before the sale plays out after it, across every conversation they connect |
| Conversation analysis | Records and transcribes calls; surfaces objections, buying signals, sentiment, talk ratio and coaching moments[9] | The foundation. AI enriches every conversation you connect with fields such as intent, root cause and resolution[4] |
| How teams use the data | Forecast pipeline, review deals, coach reps and automate follow-ups[1] | Describe the insight you want, and Rippit adds it as a field across every conversation you connect (root cause, churn risk, resolution).[4] Reuse it in dashboards and follow-up questions, and set up agents that send findings, with the evidence, to Slack or email[24] |
| Custom analysis | AI Trackers (question-based smart trackers) find concepts by meaning in calls and emails. People with the Manage AI Trackers permission (typically admins or RevOps) build them, and they consume Gong credits[12] | AI tags every conversation you connect with the fields you define, such as intent, root cause, churn signal and resolution[4] |
| What gets scanned, analyzed and acted on | Automatically captures, transcribes and analyzes calls, meetings and emails, which Gong says covers 99% of customer interactions;[9] AI agents generate summaries, update CRM fields and suggest follow-ups.[9] | 100% of conversations in scope, with nothing sampled[6][2] |
| How it handles large datasets | Turns calls, meetings and emails into structured data[9] and connects every interaction to deals and accounts in its Revenue Graph[1] | Stores every conversation you connect as structured, queryable data[3] that agents and people can enrich and aggregate across the full dataset |
| Traceability | AI Trackers mark the moments in conversations that matter, so you can filter calls in search and review those specific moments[13] | Every result is a field on a specific conversation,[3] and every agent step writes to a table you can open, so any answer can be audited |
| Repeatability | Once built, an AI Tracker is reused in search filters, streams (notifications), team insights and scorecards;[13] Gong Agents automate follow-ups, pipeline edits, enablement triggers and forecast corrections[1] | Agents you set up run hourly, daily, weekly or monthly on new conversations and send findings, with the evidence, to Slack or email[24] |
| Setup | Sales-led, with a custom proposal. Integrating your existing tech stack is included at no charge[7] | Self-serve. Connect Zendesk, Intercom, Granola, Gong and more with pre-built integrations,[10] without needing an engineer to start |
| Works with Gong | Holds these calls alongside its revenue workflows | Connects to Gong natively, so sales calls sit next to support tickets and chats[10] |
| Works with Claude | Official Gong MCP server with read-only tools for questions about a single account or deal[14] | Rippit's MCP server lets Claude, ChatGPT or Cursor query your Rippit data across every source you connect[15] |
| Public pricing | Pricing by quote: per-user licenses plus a platform fee[7] | $10 in free credits to start, no card needed; then pay as you go: $1/GB a month for storage with compute included, AI models at list price, people and agents free; Enterprise by quote[6] |
| Best starting question | "Which deals are at risk this quarter, and why?" | "Did what we promised in sales show up as friction in support?" |
Pricing checked November 13, 2026.
01 · THE DIFFERENCE
What's the difference between Rippit and Gong?
Gong helps revenue teams win and forecast deals. Rippit helps sales, product and CS teams understand what customers say to the company before and after the sale, next to the deal and account data behind it.
Gong calls itself a "Revenue AI OS."[1] It captures calls, meetings and emails, maps them to deals and accounts,[9] and runs forecasting, sales engagement, enablement and AI agents on top.[1] Gong Forecast uses interaction and activity data to predict deal outcomes.[16]
Rippit is where those conversations become shared, structured data.[2] It connects to Gong, your helpdesk and your other conversation tools,[10] reads every conversation in them and turns what it finds (intent, root cause, sentiment, churn signal, resolution) into fields you can filter, trend and build on.[4] It also brings in the pipeline, product usage and competitor research around those conversations, so a pattern in calls can be read against deals and renewals. Sales, product and CS each set up their own agents on that same foundation.[24]
Take a customer who hears in a sales call that migration takes a week. Gong records the promise and ties it to the deal. Three months later, that customer filed four tickets about a stalled migration. With Gong calls and support tickets in one place, Rippit counts how often a sales promise turns into support friction, which promise causes it, and whether it's getting worse.
BEFORE THE SALE · IN GONG
AFTER THE SALE · IN YOUR HELPDESK
"Migration takes a week."
Promise tied to the deal4 tickets about a stalled migration
Traces the churn risk back to the sales promise
Churn risk shows up in support→its cause is in the sales call→seeing both takes the whole journey
02 · SCALE
How does Rippit analyze every conversation?
Sales calls and support tickets alike, Rippit reads every conversation you connect.[2][10]
Rippit runs an AI engine built to label conversations in bulk: AI enriches every conversation you connect[2] and stores the results as structured, queryable data,[3] so analysis and agents can work across all of it. Reading every call and ticket doesn't have to be expensive. In Rippit's own coverage benchmark, it read all 1,000 transcripts in full for about six cents, the same full read that cost about $61 per question with a map-reduce approach.[23] Questions then run on those stored fields across the full dataset, instead of rereading transcripts each time, so asking the same question again returns the same answer.[23] For open-ended questions no existing field answers, Rippit also runs ad hoc worksheet analysis[3] on up to 10,000 conversations at a time, and what they find can feed new analysis and agents.
That changes three things:
1,000 conversations or 1,000,000. Rippit reads every one you connect.
Counts are complete
"What share of renewal calls mentioned a competitor, and did those accounts file more support tickets afterward?" is computed across every call and ticket in scope.[6]
New questions reach the past
Describe a new field in plain English, and Rippit applies it to past conversations in a single run.[4]
Every number is checkable
Each result is a field on a specific conversation, so you can open the conversations behind any count.[3]
03 · USE BOTH
Can you use Rippit and Gong together?
Yes. Rippit connects to Zendesk, Intercom, Granola, Gong and more, so sales calls sit next to support tickets and chats in one analysis.[10] You keep Gong for forecasting, deal reviews and coaching, and use Rippit to ask questions that span the whole customer journey.
Take a question a sales leader might bring to a pipeline review: which promises made in late-stage calls turn into support tickets after close, and which open deals are hearing them now? Gong holds the calls and the deal stages. Rippit has already read those calls alongside support tickets and chats and stored each judgment as a field.[4]
That lets it match a promise in a sales call to friction in the same customer's tickets, with deal stage and amount from your CRM when it's connected. If "promise made in sales" isn't a field yet, a worksheet analysis reads up to 10,000 conversations to answer the question now,[3] and adding it as a field applies it to past conversations in one run.[4]
Save the question as an agent, and each week it sends sales leadership the open deals hearing a risky promise, with the quotes behind them, in Slack or email.[24]
In Rippit's review of 2,345 of its own sales conversations from 2025–2026, 133 prospects said their company already uses Gong, most often for sales calls.[5] A recurring ask was to analyze those calls alongside support tickets and chats, so product feedback and churn signals show up across the whole customer journey.[5]
Using both fixes two common gaps:
- Sales and support conversations live in different tools. Gong holds what was said before the sale, and your helpdesk holds what happened after. Rippit brings the ticket and chat history into the same analysis as the calls.[10]
- Churn signals show up after the handoff. Checkr combined Rippit with Snowflake operational data to find churn signals.[17] Brex feeds predicted CSAT and churn signals from Rippit into its customer success workflows.[18]
Gong
Sales calls
Zendesk
Support tickets
Intercom
Chats
Questions that span the whole customer journey
Forecasting, deal reviews and coaching
Mass-scale enrichment, deep dives and agents you set up
133
prospects said their company already uses Gong, in Rippit's review of 2,345 sales conversations[5]
04 · CUSTOM ANALYSIS
How is Rippit's custom analysis different from Gong AI Trackers?
Gong AI Trackers find a concept by meaning rather than exact words. A tracker for discount requests catches the same ask phrased many ways.[13] People with the Manage AI Trackers permission (typically admins or RevOps) build trackers with a question-based builder, and they run across calls and emails.[12] When you publish a tracker with a past start date, Gong applies it to historical calls from that date, subject to your available credits.[12]
Rippit starts from a plain description of the insight you want and adds it as a field across every conversation in scope.[3] One pass tags each conversation with the fields you define, such as intent, root cause, sentiment, urgency, churn signal and resolution, and a new category applies to past conversations in a single run.[4] For cross-journey questions, you define a field like "promise made in sales" or "onboarding friction" once and read it across sales calls, tickets and chats.
This speed pays off with unplanned questions. When Checkr's CEO asked a late-night question about background check issues in a specific state, the team delivered a detailed report by the next morning.[17]
| GONG AI TRACKER | RIPPIT FIELD | |
|---|---|---|
| Set up by | People with the Manage AI Trackers permission (typically admins or RevOps), using a question-based builder[12] | Any user, describing the insight they want in plain language[3] |
| Reads | Sales and success calls and emails[12] | Every conversation source you connect, including tickets, calls, emails and chats |
| Looks back | Historical calls from a start date you choose, within available Gong credits[12] | Every past conversation, in a single run[4] |
05 · AI ASSISTANTS
Do Gong and Rippit have an MCP server?
Yes. Rippit supports MCP, so AI assistants like Claude, ChatGPT or Cursor can query your conversation data directly.[15] You ask in Claude, and the answer comes from fields Rippit has already added to every conversation you connect.[19]
This matters at scale. An assistant holds a limited amount of text in its context window at once,[20] so on large sets of conversations it works through retrieval, splitting or summaries. Rippit has already read every connected conversation and saved what it found as fields, so Claude starts from those results and every number traces back to specific conversations.
Gong also offers an official MCP server.[14] Its read-only tools answer questions about a single account or deal and generate account or deal briefs, and a Gong Technical Administrator creates the integration.[14] Use Gong's server for deal and account questions, and Rippit's for analysis across every conversation you connect.
Published pricing
- Free to start: $10 in credits, no card needed, plus $90 more when you add a card[6]
- Storage: $1 per GB a month, with the compute to sync, run jobs and query included[6]
- AI models and data tools: each provider's list price, no markup[6]
- People and agents: free[6]
- Enterprise: a personalized quote, with SSO, SCIM, audit logs, data region choice and custom terms[6]
Pricing by quote
Gong doesn't publish prices. Licenses are priced per user, with a platform fee based on the number of users supported, and buyers request a custom proposal.[7] Question-based AI Trackers consume Gong credits based on the data analyzed.[12]
Per user
Based on users supported
Consume Gong credits
To compare fairly, price both on the same conversation volume, sources and history.
07 · CUSTOMERS
What do teams use Rippit for?
- Brex replaced manual QA sampling with AI review of 100% of conversations and found churn risk in 3% of them. It built custom AI classifiers for onboarding friction, product gaps, sentiment and competitors, and feeds predicted CSAT and churn signals into CSM workflows.[18]
- Checkr broke dissatisfaction down into specific "atomic problems" and combined Rippit with Snowflake operational data to find churn signals. Checkr says its time from insight to action went "from weeks to hours."[17]
- Klaviyo went from reviewing fewer than 2% of its 600,000+ annual support incidents to analyzing 2.5 million conversations that had never been looked at. It analyzed a full month of product-launch conversations in two hours and uses Rippit for churn pattern analysis and chatbot performance monitoring.[21]
When should you choose Gong, Rippit, or both?
Gong
You need forecasting, deal management, sales engagement and rep coaching tied to your pipeline. For those workflows, Gong is the better choice.
Rippit
Your sales, support and CS conversations live in different tools, and you want them read together, next to the deal, usage and competitor data around them. You also want sales, product and CS to ask new questions and set up agents without building trackers or keyword rules.
Both
You run revenue on Gong and want to know what happens after the sale: whether sales promises show up as support friction, which accounts show churn signals, and why.
Frequently asked questions
Is Rippit a Gong alternative?
Not for forecasting, deal management or sales coaching. Rippit is a Growth Data Platform that complements Gong: it reads your Gong calls alongside support tickets, chats and CS calls and keeps the results as structured data your teams build on.[10]
Does Rippit integrate with Gong?
Yes. Rippit connects to Zendesk, Intercom, Granola, Gong and more, so sales and support conversations sit in one analysis.[10]
Can Gong analyze support tickets?
Is Rippit cheaper than Gong?
Do I need to build trackers or keyword rules to use Rippit?
No. AI classifies conversations across multiple dimensions, and new categories apply to past conversations in one run.[4]
Does Rippit work with Claude?
Yes. Rippit supports MCP, so Claude can query your conversation data directly.[15]
Is Rippit only for support?
Can I trace a number back to the conversations behind it?
Yes. In Rippit, every result is a field on a specific conversation,[3] so you can audit any answer by opening the conversations and fields behind it.
Is Rippit the same company as MaestroQA?
Yes. MaestroQA is now Rippit: same founders, with a rebuilt AI-first platform. The company moved from maestroqa.com to rippit.com, and existing customer data, rubrics and integrations carried over.[22] Some Rippit help center articles still appear under the MaestroQA name and domain.
Sources
Rippit was formerly MaestroQA. Some sources below are hosted on the MaestroQA help center (help.maestroqa.com).[22]
- Gong, homepage: https://www.gong.io/
- Rippit, homepage: https://www.rippit.com/
- Rippit, How It Works: https://www.rippit.com/how-it-works
- Rippit, Conversation Taxonomy playbook: https://www.rippit.com/playbooks/conversation-taxonomy
- Rippit first-party research: AI analysis of 2,345 Rippit sales conversations (2025–2026), run on Rippit's own conversation analytics platform. 133 of 2,345 prospects (6%) said their company uses Gong.
- Rippit, Pricing: https://www.rippit.com/pricing
- Gong, Pricing: https://www.gong.io/pricing/
- Gong, Customer Success: https://www.gong.io/customer-success/
- Gong, Conversation Intelligence: https://www.gong.io/conversation-intelligence/
- Rippit, Integrations: https://www.rippit.com/integrations
- Gong Collective, Zendesk integration: https://collective.gong.io/integrations/zendesk
- Gong Help Center, Smart tracker FAQs: https://help.gong.io/docs/smart-tracker-faqs
- Gong Help Center, AI Tracker: https://help.gong.io/docs/understanding-ai-tracker
- Gong Collective, Claude MCP Client: https://collective.gong.io/integrations/claude-mcp-client
- Rippit, The Complete Guide to Conversation Analytics: https://www.rippit.com/playbooks/the-complete-guide-to-conversation-analytics
- Gong, Forecast: https://www.gong.io/forecast/
- Rippit, Checkr customer story: https://www.rippit.com/customer-story/checkr
- Rippit, Brex customer story: https://www.rippit.com/customer-story/brex
- Claude Help Center, Get started with custom connectors using remote MCP: https://support.claude.com/en/articles/11175166-get-started-with-custom-connectors-using-remote-mcp
- Claude Docs, Context windows: https://platform.claude.com/docs/en/build-with-claude/context-windows
- Rippit, Klaviyo customer story: https://www.rippit.com/customer-story/klaviyo
- Rippit, "MaestroQA is now Rippit": https://www.rippit.com/news/maestroqa-is-now-rippit
- Rippit, The Coverage Benchmark, Part 8: Rippit: https://www.rippit.com/research/coverage-benchmark-rippit
- Rippit docs, Pipelines: https://docs.rippit.com/pipelines/overview